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Aspects of online teaching and their connection with positive experience and motivation among teacher training students: mixed-method findings at the beginning of COVID-19


The prescribed changeover to online teaching due to COVID-19 / SARS-CoV-2 in the 2019/20 summer semester offered the unique opportunity to research online teaching in teacher training. The aim of the present study was to be able to make recommendations for online teaching in the teaching profession. That is why we have identified beneficial / hindering aspects of online teaching (Study 1) and their connection with the positive / negative experience by students and their learning and achievement motivation analyzed (Study 2). In Study 1 (n = 75), qualitative aspects of online teaching were surveyed that teacher trainee students experienced as beneficial or obstructive for subjective learning success (open data & materials: https://osf.io/7knhj/). The study was preregistered at https://osf.io/438p6/ prior to data collection. We identified 39 aspects using qualitative content analysis. In Study 2 (n = 855), these aspects were collected for positive & motivating or negative & demotivating courses and related to the situational learning and achievement motivation (SELLMO) of the teacher training students (open data & materials: https://osf.io/87v5y/) . The study was preregistered at https://osf.io/rj5f9/ prior to data collection. Analyzes with BEST showed seven aspects with very large differences (|d| > 3), 18 with large differences (3> |d| > 0.8), seven with medium / small differences (0.8> |d| > 0.2), and seven with no differences (2> |d|) between positive & motivating or negative & demotivating courses. Multi-group pathway analyzes identified 13 aspects related to learning and achievement motivation. Our results contribute to understanding online teaching in teacher training. They show that online teaching can be optimized and controlled depending on the goal - positive experience, learning and achievement motivation, both.


In the summer semester 2019/20 it was degreed that universities switch to purely online learning due to COVID-19 / SARS-CoV ‑ 2. This switch offered the unique opportunity to study online learning in teacher education. The aim of the present study was to give recommendations for online learning in teacher education. To this end, we identified aspects of online learning that students experienced beneficial / impeding (study 1) and analyzed their relation to the positive / negative experience of students and their goal orientation (study 2). In study 1 (n = 75), we asked students for aspects of online learning that they experienced as beneficially / impeding for their perceived learning gain (open data & materials: https://osf.io/7knhj/). The study was pre-registered before data collection (https://osf.io/438p6/). We identified 39 aspects by means of qualitative content analysis. In study 2 (n = 855), these aspects were rated for positive & motivating or negative & demotivating courses and related to the situational goal achievement (SELLMO) of the teacher education students (open data & materials: https://osf.io/87v5y/). The study was pre-registered before data collection (https://osf.io/rj5f9/). Analyzes with BEST showed seven aspects with very large differences (|d| > 3), 18 with large differences (3> |d| > 0.8), seven with medium / small differences (0.8> |d| > 0.2), and seven without differences (2> |d|) between positive & motivating or negative & demotivating courses. Multigroup path analyzes identified 13 aspects that were related to goal orientation. Our results contribute to the understanding of online learning in teacher education. They highlight that online learning can be optimized and steered depending on the objective — positive experience, goal orientation, or both.


The COVID-19 / SARS-CoV-2 legally prescribed conversion (information from the Austrian Federal Government of March 10, 2020) of all Austrian university teaching to online teaching (teaching that takes place exclusively via the Internet, see "Purely Online Learning" according to Means et al. 2013) offered the unique opportunity to research the added value and pitfalls of online teaching in teacher training.

For educational science research at this time, it should be borne in mind that the conversion of university teaching to online teaching opened up opportunities for research, but also set limitations. The chances were that all Parts of the teacher training had to be carried out online. This eliminated selection effects that were inevitable for studies under normal circumstances: all Teachers had to switch to online teaching; all Students had to take part in online teaching. Nevertheless, there were limitations. On the one hand, there was content from courses for which no online solution could be found even in this exceptional situation (e.g. sports and creative week). On the other hand, the suddenness of the change limits that studies with comparison groups could be planned and implemented; So it was possible to research the online teaching, but not to compare it with a comparison group with conventional or other implementation.

We pursued the goal of identifying aspects of online teaching that were perceived by students as helpful / hindering and to examine how these aspects were related to the experience of the courses and the students' motivation to learn and achieve (LLM). It was our concern to be able to give concrete recommendations for future online teaching in the teaching profession.

Online teaching: Theoretical background and limitations of previous studies

Even before Covid-19, there were numerous theories about online teaching in literature. However, experts criticize the fact that theories only address selected aspects of online teaching (for a critical overview: Arghode et al. 2017). This phenomenon can also be found in the literature specifically on online teaching in the teaching profession, where studies always only deal with individual aspects of online teaching in the teaching profession (for an overview: Carrillo and Flores 2020). This lack of an overarching theory can also be seen in the fact that studies are often limited to individual theories (e.g. “Communities of Inquiry”) and, accordingly, only very specific content is researched (e.g. student-teacher interaction: Shea et al . 2010). Still others use a much broader theoretical framework for online teaching (e.g. principles of good teaching or the EESS model), but then use this to promote only one aspect of university teaching, such as self-regulated learning (Astleitner 2006) or the perceived benefit (Al-Fraihat et al. 2020). Against this background, more and more qualitative studies are required (Arghode et al. 2017) in order to get an insight into online teaching that is not limited by the glasses of a specific theory and to the aspects prescribed by it. Such qualitative studies would also do justice to the claim to better capture the quality of student learning experiences (e.g. Robinson and Hullinger 2008).

Furthermore, there were numerous concepts and corresponding evaluations of online teaching even before Covid-19. Unfortunately, these concepts lack empirical verification (e.g. Hartman and Morris 2019). Often only very narrow success criteria are focused (e.g. “student engagement”: Ornelles et al. 2019). It also shows that the suggestions for evaluating these concepts have the same pitfalls as evaluating face-to-face teaching: The focus is on self-assessed competencies (Paechter 2006). In summary, further empirical evidence is required, which should go beyond narrow success criteria and self-assessed competencies.

Finally, one of the strong limitations mentioned at the beginning can also be seen in the existing literature: the selection effects in course content. Findings on online teaching are often gained where online teaching content is taught (e.g. "Technology Tools and Integration for Teachers": Cho et al. 2017). This limitation probably arises from the fact that it makes pedagogical sense to teach this content online and that these lecturers are fit in online teaching. The prescribed conversion of the entire University teaching based on online teaching offered the opportunity to circumvent this limitation.

Online teaching: empirical findings

Meta-analyzes have so far shown no advantages or disadvantages of online teaching compared to face-to-face teaching (ρ ≈ 0: Bernard et al. 2004; ρ = −0.01: Machtmes and Asher 2000; ρ = 0.10: Zhao et al. 2005). Recommendations for online teaching are based on modest effect sizes, with hardly any new knowledge about good teaching being generated. In Bernard et al. (2004) showed that asynchronous online teaching has a slight advantage (G = −0.10), but synchronous has a slight disadvantage (G = 0.05). Machtmes and Asher (2000) point out that two-way communication between teachers and students is beneficial. The most concrete recommendations are given by Zhao et al. (2005), e.g. B., some course content is easier to implement online than others; inter-individual differences between the learners influence the learning success. Zhao et al. emphasize the importance of applying existing knowledge about the effectiveness of face-to-face teaching to online teaching.

The most recent meta-analysis known to us showed that academic success in face-to-face teaching and online teaching is comparable (d = 0.05), but face-to-face teaching has small disadvantages compared to mixed forms ("blended learning": d = 0.33; Schneider and Preckel 2017). This result was already shown in Means et al. (2013; G = 0.35), although it was also shown that this difference is confused with more learning time, resources and interactions between learners.

Taken together, meta-analyzes do not provide any reliable recommendations for the design of online teaching in order to be equally effective or even better than classroom teaching that go beyond previous knowledge about good teaching. In addition, it remains to be seen how teachers actually implement online teaching (Arghode et al. 2017). Against this background, it seems expedient to identify a wide range of aspects that students experience in online teaching. Such aspects can be criteria of good teaching, can be a subset of these, can go beyond, or can take up specifics of online teaching for concrete implementation.

Learning and achievement motivation

In multimedia psychology it was postulated a long time ago that motivational factors - such as e.g. B. the LLM - can be significantly influenced by the application of new media (Seel and Ifenthaler 2009). Previous studies are limited to the fact that the importance of motivational factors for z. B. Self-regulated learning in open online learning environments is examined (e.g. Song and Bonk 2016). Unfortunately, there are no studies that examine the interrelationships between LLM in curricularly planned and specified course formats.

Meta-analyzes showed that LLM are important motivational factors for academic success (| 0.12 | ≤ ρ ≤ | 0.14 |, “goal orientations”: Richardson et al. 2012). LLM are already linked to academic achievement in secondary education; Above all, learning goals even go beyond intelligence and personality (Steinmayr et al. 2011; Steinmayr and Spinath 2009). In the teacher training course, less favorable career choice motives go hand in hand with less favorable LLM (learning objectives: r = 0.43; Avoidance performance goals: r = -0.32; Koenig et al. 2018). Furthermore, there are connections between the LLM and the educational knowledge of students (learning objectives: r = 0.18; Avoidance of work: r = -0.15; König and Rothland 2013). Finally, it was also shown that the LLM are relevant for the later teaching profession, so they are related to burn-out and the interest in teaching (e.g. avoidance of work: r = 0.31 or r = −0.35: Retelsdorf et al. 2010).

The LLM can be seen and assessed as a dispositional or situational construct (Payne et al. 2007). LLM can be dispositional for school or studies, for example (SELLMO-S or SELLMO-ST: Spinath et al. 2002). Depending on the situation, LLM can be related to a specific task (Button and Mathieu 1996) or a specific course (Harackiewicz et al. 1997). This differentiation had to be taken into account with LLM, because the aim of this study was not to ascertain the LLM as a stable disposition of the students. For the purposes of this study, the LLM had to be related to the legally prescribed online teaching in teacher training. Furthermore, a situational LLM means that interventions on the course level can be planned in order to change the LLM.

The present study

Against this background, it was our goal to first qualitatively ascertain which aspects of online teaching student teachers experience as beneficial or as a hindrance to their subjective learning success. In Study 1 Students from different semesters and degree programs were openly asked about online teaching during the changeover due to COVID-19 / SARS-CoV-2 (summer semester 2020). In order to get the broadest possible overview, a sample of teacher training students was recruited from different courses and in different study phases.

This brings us to the demand for a better record of the quality of student learning experiences (e.g. Robinson and Hullinger 2008) and to investigate how online teaching is actually implemented (Arghode et al. 2017). Furthermore, a qualitative approach is not tied to a preferred theory and can therefore represent a broader framework. Our approach also makes it possible to go beyond the existing criteria of good university teaching by showing aspects that are specific to online teaching and have not yet been taken into account. After all, our study is not subject to the limitation that only specific course content was implemented as online teaching, but rather all Contents of the teacher training course.

Building on this, it was our aim to be able to make specific recommendations as to which aspects should be considered Study 1 are suitable for increasing the quality of online teaching in the teaching profession. Study 2 should accordingly select those aspects of online teaching Study 1 identify which (1) differentiated positive & motivating courses from negative & demotivating courses and (2) related to the LLM of the students. The former was motivated by the fact that courses should generally be experienced in a positive & motivating manner (Helmke 2012), and therefore should be able to differentiate between relevant aspects of online teaching and how a course was experienced. For this purpose, the aspects (1) for positive & motivating or negative & demotivating courses were collected and (2) related to the LLM (learning goals, approach performance goals, avoidance performance goals and avoidance of work) of the students.

We first analyzed which aspects of Study 1 differed between positive & motivating courses and negative & demotivating courses. Then we exploratively analyzed which of these aspects were related to the situational LLM. Of particular interest were those aspects which on the one hand could differentiate between the two courses and on the other hand were more pronounced in learning goals and Proximity Performance Goals and a lower value in Avoidance Performance Goals and Work avoidance went hand in hand (or vice versa).

Study 1: Aspects of Online Teaching


For Study 1 we decided on a content-analytical approach (Mayring 2014) in order to openly explore the subject and the results for the instruments of Study 2 to use (Mayring 2015). The source material was in the form of texts. The material to be evaluated was created in the context of courses in which students answered three open questions in writing. These related to the entire teaching in the semester affected by COVID-19 / SARS-CoV-2 (summer semester 2020). The first question related to the different formats of the course implementation, the second and third to aspects that were experienced as beneficial or hindering for the subjective learning success. The complete formulations can be found in the appendix (see section Study 1: Open questions for students). The data was collected anonymously and participation in the study was voluntary. The source material was available in written digital form. The documents were prepared for data evaluation (consecutive numbering of the documents, uniform sales control, file format, etc.). In the interests of transparent procedural documentation (Mayring 2016), the study was preregistered before the data was collected: https://osf.io/438p6/. All data and materials can be found at https://osf.io/7knhj/.


The sample consisted of 75 teacher training students in Austria (80% female, 20% male). 68% were in the bachelor's degree and 32% in the master's degree; either in the SEK General Education (32%), SEK Vocational School (1%), SEK Department of Nutrition (27%), SEK Department of Information and Communication (13%,) or primary level (27%). They were on average in the 5.4 semester (SD = 2.4; Span: 1–10).


The text material was analyzed by means of qualitative content analysis with inductive category formation in QCAmap (Mayring 2014) analyzed for beneficial or hindering aspects for one's own learning. Based on the specifications of the QCAmap web application, basic settings were made (coding unit, context unit, evaluation unit) and a research question was created for beneficial and one for hindering aspects. In the first material pass, content-bearing text passages were identified and coded (second and third author). When coding, the coders (as they themselves are lecturers with online courses) explicitly took care to hide possible assumptions and to formulate categories close to the text. After 1/3 of the material, the category system was checked again (revision) and the category definition was adjusted slightly. The entire starting material was completely analyzed in a second material run with the final category system. The intercoder reliability was then checked by an independent second coder (first author). 10% of the texts were selected at random and subjected to a detailed check by the second coder. The minor inconsistencies were discussed in a coding conference and taken into account in the analysis result (Mayring and Brunner 2010). In the next step, the categories were clustered in terms of content and combined into aspects (main categories). The result is a list of the beneficial and hindering aspects and how often these have been identified in the material.

Results & discussion

We have 611 categories inductive to 20 beneficial (f = 366) and 25 obstructive (f = 245) Aspects for the subjectively experienced learning success summarized, with seven aspects being experienced in the opposite form as beneficial and hindering (Tab. 1, 2 and 3). In the following, those aspects are briefly explained which, in our opinion, are not self-explanatory.

Participation in Video conferencing (f = 85) described the possibilities and advantages of direct communication as a beneficial aspect. The aspect Individual work / individual orders (f = 11) included the opportunity to deal intensively with content independently of others. The aspect clear structure (f = 10) mainly referred to the structured preparation in learning platforms. The aspect Actively involve students (f = 6) meant that lecturers involved students directly in the course (e.g. asking questions directly).

The obstructive aspect Confusing / missing information about the course (f = 20) described that teachers transmitted too little or too much information in a confusing manner. The aspect technical problems (f = 16) mostly related to a poor internet connection. Working with texts (f = 8) included text work in self-study without the possibility of discussing it with others. The lack of time management by the teacher (f = 4) referred to very short-term information transfers.

The aspect Group work became both conducive (f = 4) as well as obstructive (f = 18) experienced. Helpful when the workload could be divided well or the group did not consist of more than three people. Group work z. B. described with more than three people when group work was poorly coordinated by the teacher and / or fellow students could only communicate electronically with each other.

Study 2: Aspects of online teaching & learning and achievement motivation


The study was pre-registered prior to data collection: https://osf.io/rj5f9/. All data and materials can be found at https://osf.io/87v5y/.


The student teachers were recruited using an online survey that was sent out. 1300 students opened the survey, 67% completed it. We excluded: three students because the processing time was too short (<4 min), nine due to double processing, two because they did not study teaching and finally two because they stated the negative & demotivating course, no such in the semester in question to have had.

The final sample of 855 teacher training students in Austria (84.6% female, 15.2% male, 0.1% cis male, 0.1% no answer) had a median age of 23 years (Q25 = 21; Q75 = 25; 2 missing information) and on average 24.15 (SD = 5.48; Span: 18–65); the median study progress was the 4th semester (Q25 = 2; Q75 = 6; 2 missing information) and on average the 4.61 semester (SD = 2.45; Span: 1–16). 48.5% were students at the secondary level and 50.4% at the primary level (1.1% missing information). 81.4% of the students were in the Bachelor's degree and 18.6% in the Master's degree. 62% named a teacher training college and 38% a university as their home institution.


Students were asked to recall a course from the semester affected by COVID-19 / SARS-CoV-2 (summer semester 2020). The first thing to think about was the idea of ​​a course. In order to get deeper into it, a maximum of three keywords should be given for the course. The spontaneous association of the keywords corresponds to an autobiographical memory task (autobiographic memory task, e.g. E.g .: McFarland and Buehler 1998; Raes et al. 2003). The empathy was decisive for the fact that the aspects and the LLM related to the course could be assessed.

The teacher training students were randomly divided: they were given either the autobiographical reminder task related to a course that they had experienced particularly positive & motivating for their learning or related to one that they had experienced particularly negative & demotivating for their learning. The exact instructions for this are shown in Fig. 1. 50.4% of the students were the condition positive & motivating courses randomly assigned, 49.6% of the condition negative & demotivating courses.

We compared the subsamples with regard to demographic and study-related variables. There were no differences between women and men (χ2[1] = 1,69; p = 0.194) or find in age (∆M = 0.21 [−0.18; 0.59], d = 0.01; evaluated with BEST: see description and use of BEST in Section 3.1.5). We couldn't find any differences in the main course (χ2[1] = 0,08; p = 0.772), in the course (χ2[3] = 2,38; p = 0.304), in the semester during studies (∆M = 0.28 [−0.05; 0.61], d = 0.12) or in the home institution (χ2[1] = 0,88; p = 0.347). We therefore assumed the random allocation to the two conditions and thus the comparability of the partial samples.

After the autobiographical memory task, a. collected which course type the respective course was (s.Information about the course in Sect. 3.1.3). Here it was shown that the five types of courses and the category “other” were not evenly distributed in the two conditions (χ2[5] = 15,949; p = 0.007). Internships were more often in the positive & motivating condition (4.8% vs. 2.3%) and lectures with practice were more often in the negative & demotivating condition (3.7% vs. 6.5%). Since these two types of courses were rarely chosen (for comparison: seminars were 53.9% of all courses: 26.9% vs. 27%), we assumed that the conditions were comparable.


Demographic and study-related variables

The students were asked about gender (open question), age (in years) and study-related variables. These were: main course (Bachelor / Master), course of study (primary level, secondary level general education, secondary level vocational education, other), study progress in semesters and which university type your home institution was (main enrolled at a university or teacher training college).

Information about the course

The students should indicate which area the course was from (educational sciences, subject didactics, specialist science, school practice, other), to which course type it was assigned (VO = lecture, VU = lecture with exercise, SE = seminar, PR = internship, UE = Exercise, other) and at which type of university it was held (university, teacher training college).


To the out Study 1 extracted aspects, we formulated items. We excluded those aspects that were mentioned too seldom by students, did not find a consensus in the research team or related to personal challenges of the COVID-19 / SARS-CoV-2 situation. If beneficial and hindering aspects were their opposite, the more frequent one was chosen (e.g. Clear work assignments / tasks for the teachers was cited as beneficial more often than imprecise explanation of work orders as a hindrance). Then we formulated items from the aspects. The item stock of the items was "In this course ..." in order to relate the aspects to the relevant course. The response format was 1 =does not apply to 4 =true. The complete list of items can be found in descriptive Tab. 4 (see Appendix Tab. 5 for a comparison of the aspects and items).

Learning and achievement motivation

The LLM was awarded the Scales for recording learning and achievement motivation (SELLMO: Spinath et al. 2002). The SELLMO is a German-language inventory for recording the LLM, which has proven itself in educational studies (e.g. König et al. 2018; Steinmayr et al. 2011; Steinmayr and Spinath 2008, 2009). We have adjusted the item master "In this course ..." in order to raise the LLM for the relevant course.

The SELLMO comprises four scales: learning goals, approach performance goals, avoidance performance goals and work avoidance. The four scales have 31 items (5-level answer format: 1 =not true at all to 5 =exactly). The internal consistencies of the four scales were 0.76 ≤ α ≤ 0.88 for the positive & motivating LV and 0.81 ≤ α ≤ 0.91 for the negative & demotivating LV.

A sample item for learning goals is "...I want to gain a deep understanding of the content". A sample item for Proximity Performance Goals is "... I want to do work better than others". A sample item for Avoidance Performance Goals is "... my aim is not to attract attention with stupid questions". A sample item for Work avoidance is "... my aim is to keep the workload low at all times“.

Three items have been adjusted. We have two items from Work avoidance adapted, as they were not applicable in the original to individual courses in online teaching. We changed “… Not having to do any work at home."To"... not having to do any work beyond the course time." and "... to get through school / studies with little work."To"... to get through the course with little work.“From. For an item from Avoidance Performance Goals we changed the word “lecturer” to “teacher” in order to establish the contextual reference and to pay attention to a gender-neutral formulation.


Participation in the survey was voluntary and anonymous and could be canceled at any time without giving reasons. The survey was sent to teacher training students in Austria and could be processed online via LimeSurvey.

All students first provided demographic and study-related information and were then randomly assigned to the autobiographical memory task. Then the information about the course was given, followed by the assessment of the aspects and the SELLMO.


Manipulation check

First, we compared the LLM of the two conditions. We checked mean value differences with Bayesian estimation approach (BEST: Kruschke 2013) of the relevant R-package (Kruschke and Meredith 2018) instead of conventional t-tests. BEST gives a ‑ posteriori distributions forM. and SD and also a ‑ posteriori distributions of ∆M., ∆SD andd. BEST checks ∆M., ∆SD andd in an analysis. BEST goes beyond the potential of a t-test (this can only ∆M. check for significance) and that of a Bayesian factor (this indicates the ratio of the probabilities of different assumptions, often that there is a vs. no difference). We used BEST with the default settings (Markov Chain Monte Carlo length 100,000 and no thinning) and uninformed priors, as no information on the distributions of the LLM in online teaching could be derived from previous studies. We orient ourselves to the interpretation of the results 95% High Density Interval to check whether z. B. ∆M. ord were different 0 (0 not in the interval) or notFootnote 1. The BEST results thus provide information about whether aspects differed on average or not (which is often attempted to answer with a t-test), and provided us with an estimate of the effect size for the comparison of the mean valuesd.

learning goals and Proximity Performance Goals should be higher for positive & motivating courses than for the negative & demotivating condition; For Avoidance Performance Goals and Work avoidance it should be the other way around. These differences in mean values ​​should confirm the differentiated effect of the different autobiographical memory tasks.

The aspects in the conditions

In the next step, we described and compared the positive & motivating course and the negative & demotivating course with BEST on all aspects. We looked at the differences exploratively.Footnote 2

Aspects & learning and achievement motivation

First we looked at the correlation between the aspects and the LLM for the two courses. For the further analyzes, we only included aspects that correlated with LLM scales (p <0.05 and r> 0.10).

The aspects were linked to the LLM by means of path analyzes. All paths were allowed (covariances within the aspects and within the LLM; regression paths from the former to the latter). First, this path model was calculated for both courses using multi-group path models. Afterwards we excluded those aspects which were insufficiently related to the LLM (all β <0.1).Footnote 3 Finally, we gradually introduced restrictions across the courses to check whether the relationships between the courses differed. We set the restrictions depending on the numerical similarity of the relationships (the most similar first, etc.).

The models were made with lavaan (Rosseel 2012) calculated. A WLSMV estimator was used; on the one hand, because this estimator is better suited as an ML estimator for Likert scales with a small number of values ​​and on the other hand, to be able to use robust standard errors due to the skewed distributions (skewness and kurtosis> | 1 |).

The explanation of variance was used as an evaluation criterion for the models. We determined the significance of the paths using their standard errors (critical ratio test; p <0.05). For the model comparisons, we compared the χ2-Statistics. If the ∆χ is not significant2 (p ≥ 0.05) the model was accepted with restrictions.


Manipulation check

The descriptive statistics of LLM and their comparison between positive & motivating courses and negative & demotivating courses are listed in the upper part of Table 4. There was a big difference in learning goals (d = 0.60), and a small difference in Avoidance Performance Goals (d = −0.37) and Work avoidance (d = −0.48). There was no difference for approximate performance goals (d = 0.05). With the exception of the Proximity Performance Goals, the Manipulation Check showed the expected results.

The aspects in the conditions

In the next step it was compared which aspects differ between positive & motivating courses and negative & demotivating courses (based on Cohen (1988) expanded to include the category “very large differences”). There were very big differences (|d| > 3) for seven aspects (e.g. work orders were clearly formulated), large differences (3> |d| > 0.8) for 18 aspects (e.g. the teacher was available), medium / small differences (0.8> |d| > 0.2) for seven aspects (e.g. lack of social interaction with other students) and no differences (0.2> |d|) for seven other aspects (e.g. too many different tools were used). The descriptive statistics and the comparison between the two courses are given in the lower part of Table 4.

Aspects & learning and achievement motivation

In the next step we analyzed how the aspects related to the LLM for both courses.The separate analysis for the two courses was also reinforced by the fact that there were 76 significant correlations for positive & motivating courses, and only 30 for negative & demotivating courses (see Table 6; see Table 7 for the intercorrelations of all aspects). First, we excluded six aspects that did not correlate with any of the LLM scales for either course (p ≥ 0.05 or r ≤ 0.10; see table 6 in the appendix). The remaining 33 aspects were used in the multi-group path model to predict the LLM.Footnote 4 In the next step in this multi-group path model, we set those paths to zero that had a low correlation (β <0.1). These restrictions did not lead to a deterioration of the model (∆χ2[229] = 209,14; p = 0.674). We then excluded all 20 aspects from the analyzes where there were no longer any paths to LLM and repeated the analyzes. In the last step, eight paths were equated, which had a numerically similar relationship for both courses. If one of the paths to be equated had already been set to zero, both were set to zero (five of the eight paths). For one aspect, the two paths became too Proximity Performance Goals set to zero, because the exclusion of the unrelated aspects made their context disappear. This final multi-group path model with restrictions explained the data no worse than the model without these restrictions (∆χ2[10] = 80,755; p = 0.636). The remaining aspects and their relationship to LLM are shown in Fig. 2.

As Fig. 2 shows, the relationships between the aspects and the LLM scales were different for the two courses. The aspects were better suited to explain the variance of the LLM of the positive & motivating courses (3.6–12.7%) than of the negative & demotivating courses (1.2–6.5%). Overall, three paths were identical between the courses: “There was no social interaction with the teacher” learning goals, "I had technical problems" Avoidance Performance Goals, and “there was a combination of video conferencing and the use of a learning platform” Work avoidance. All other paths only showed up for one of the two courses.

There were also suppression effects. All significant paths of “were work orders clearly formulated” and “were materials and tasks structured” showed up in the path model, but not in the correlations. Likewise, the significant paths of “lack of social interaction with the teacher” for the positive & motivating course were not shown in the correlations. Further suppression effects for the positive & motivating course were the connection between "was there a combination of video conference and use of a learning platform" and Work avoidance or between “there was no social interaction with other students” and Proximity Performance Goals.

General discussion

The aim of the present study was to be able to make recommendations for online teaching in the teaching profession. We have therefore identified beneficial / hindering aspects of online teaching and analyzed their connection with the LLM. Fig. 3 shows how these aspects differ between positive & motivating or negative & demotivating courses and how, depending on this, their connection with the LLM turned out.

As Fig. 3 illustrates, the aspects show very differentiated relationships. Aspects emerged that are general quality criteria for good teaching (or their opposite: e.g. poorly thought-out work assignments) that differentiated whether students experienced courses as positive & motivating or negative & demotivating and related to the LLM. At the same time it became apparent that these aspects do not have to be related to the LLM (e.g. transparent course objectives). This is consistent with the findings that if students rate courses positively, they still no longer perform (Uttl et al. 2017). Derived from this, three subject areas for recommendations on online teaching are possible with this study:

  1. 1.

    General recommendations,

  2. 2.

    Recommendations for the positive experience of courses by students and

  3. 3.

    Recommendations for the LLM of the students.

General recommendations

Many of the in Study 1 The identified aspects cover areas that are not only specifically important for online teaching, but also for university teaching in general (Schneider and Mustafić 2015). Compared with the meta-analysis on university teaching by Schneider and Preckel (2017), it became apparent that some of these aspects are strongly related to academic performance. For example, the structuring of the teaching (poorly thought out work orders, structured materials / tasks) is in the 105-variable list by Schneider and Preckel in 3rd place, the clarity of the work orders (clearly formulated work orders) in 4th place, and the accessibility of the Teacher (teacher contactable) in 11th place. These findings are in line with previous meta-analyzes of online teaching, whose recommendations hardly go beyond previous findings on good teaching (cf. Bernard et al. 2004; Machtmes and Asher 2000; Means et al. 2013; Zhao et al. 2005). Online teaching should therefore be based on general quality criteria of university teaching.

In addition, the qualitatively obtained knowledge is output Study 1 concrete recommendations on how general quality criteria for university teaching can be implemented in practice in online teaching so that students perceive them as being conducive to learning. At this point, for reasons of space, only examples are mentioned: When using learning platforms, care should be taken that the content is mapped in a logical structure and that work orders and the associated deadlines are clearly displayed. With regard to the deadlines, it is important to ensure that students can use an individual schedule. In addition, it should be specified and communicated in advance which medium (e.g. learning platform, e-mail, online meeting) is used for which type of information (e-mail, e.g. only for sending reminders). Discussion forums should be set up to ensure smooth and fast communication and to enable exchange among students. Audio files and / or videos made available enable students to play them back several times and have been described as particularly conducive to learning. When switching from face-to-face teaching to online teaching, teachers should make sure that they correctly assess the workload of online work assignments and that (non-) compliance with the course times is clarified in advance.

Recommendations: positive & motivating experience by students

Fig. 3 gives recommendations as to which aspects are likely to lead students to experience courses in online teaching as positive & motivating. In addition to general quality criteria for university teaching, the greatest effects were found that there was a lack of oral input from teachers and that ambiguities were difficult to clarify. It should therefore be borne in mind that ambiguities and misunderstandings in online teaching, in contrast to face-to-face teaching, cannot be quickly cleared up in personal communication. These findings expand existing recommendations on online teaching, namely how important it is to precisely specify information in online teaching (Seel and Ifenthaler 2009). Therefore, teachers should be more sensitive to the fact that materials and work assignments in online teaching must be more self-explanatory than in face-to-face teaching.